关键词: 普适计算, 上下文一致性错误, 受控实验

Abstract: Context-aware applications are subject to inevitable context inconsistencies due to various reasons including unpredictable and uncontrollable sensing noises in pervasive computing environment. These inconsistencies can lead to applications abnormal or even failure. Various approaches have been proposed to automatically resolve the detected context inconsistencies for such applications. However, it is not clear whether maintaining consistency for contexts at a data level can indeed improve an application’s quality at a software level. This paper designs a controlled experimentation to empirically study this problem, based on a group of selected context-aware robot car applications. Then, this paper compares and reports the benefits and side effects of different context inconsistency resolution approaches. The experimental results can help developers understand how context inconsistency and its resolution impact context-aware applications, as well as improving the dependability for these applications.

Key words: pervasive computing, context inconsistency, controlled experimentation